Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks

doi:10.3901/JME.2019.07.009

Abstract

Abstract: As the vibration signal of the planetary gearbox is usually nonstationary, a significant level of prior knowledge and diagnostic expertise is required to engineer and interpret features for fault diagnosis. In order to achieve intelligent diagnosis of the planetary gearbox, an intelligent fault diagnosis method based on empirical mode decomposition (EMD) and deep convolutional neural networks (DCNN) is proposed. Firstly, EMD is used to decompose the vibration signal to obtain intrinsic mode function (IMF) components. Then the IMFs with obvious fault character are fused through DCNN and features are extracted automatically. Finally, the learned features serve as the input parameters of classifier to classify working condition, and the atomization of the planetary gearbox fault diagnosis can be implemented. The experimental results show that the method can classify the working state and fault type of the planetary gearbox accurately and effectively.

Key words: deep convolutional neural networks, empirical mode decomposition, fault diagnosis, planetary gearbox

CLC Number:

TH17

HU Niaoqing, CHEN Huipeng, CHENG Zhe, ZHANG Lun, ZHANG Yu. Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks[J]. Journal of Mechanical Engineering, 2019, 55(7): 9-18.

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